Image Retrieval using Equalized Histogram Image Bins Moments

Content Based Image Retrieval (CBIR) operates on a totally different principle from keyword indexing. Primitive features characterizing image content, such as color, texture, and shape are computed for both stored and query images, and used to identify the images most closely matching the query. There have been many approaches to decide and extract the features of images in the database. Towards this goal we propose a technique by which the color content of images is automatically extracted to form a class of meta-data that is easily indexed. The color indexing algorithm uses the back-projection of binary color sets to extract color regions from images. This technique uses equalized histogram image bins of red, green and blue color. The feature vector is composed of mean, standard deviation and variance of 16 histogram bins of each color space. The new proposed methods are tested on the database of 600 images and the results are in the form of precision and recall